Epstein Secrets

ited in the DOJ Report. See DOJ Report at 38-39 nn.20-24. " See DOJ Report at 28 (discussing multiple algorithmic measures of racial bias). 15 See Melissa Hamilton, Debating Algorithmic Fairness, 52 UC DAVIS L REV. ONLINE 261 (2019); Jay P. Singh, Predictive Validi?Perfonnance Indicators in Violent Risk Assessm

ll and 58% of men);' that high scores are likely driven by static factors such as age of first conviction and criminal history 26 See Richard Berk, Acturag, and Fairness for Juvenile Jmike Risks Assessments, 16 J. EMPIRICAL LEG. STUD. 175, 184 (2019). 26 See DOJ Report at 29 (referring implicitly to wh

ited in the DOJ Report. See DOJ Report at 38-39 nn.20-24. " See DOJ Report at 28 (discussing multiple algorithmic measures of racial bias). 15 See Melissa Hamilton, Debating Algorithmic Fairness, 52 UC DAVIS L REV. ONLINE 261 (2019); Jay P. Singh, Predictive Validi?Perfonnance Indicators in Violent Risk Assessm

ll and 58% of men);' that high scores are likely driven by static factors such as age of first conviction and criminal history 26 See Richard Berk, Acturag, and Fairness for Juvenile Jmike Risks Assessments, 16 J. EMPIRICAL LEG. STUD. 175, 184 (2019). 26 See DOJ Report at 29 (referring implicitly to wh

ited in the DOJ Report. See DOJ Report at 38-39 nn.20-24. " See DOJ Report at 28 (discussing multiple algorithmic measures of racial bias). 15 See Melissa Hamilton, Debating Algorithmic Fairness, 52 UC DAVIS L REV. ONLINE 261 (2019); Jay P. Singh, Predictive Validi?Perfonnance Indicators in Violent Risk Assessm

ll and 58% of men);' that high scores are likely driven by static factors such as age of first conviction and criminal history 26 See Richard Berk, Acturag, and Fairness for Juvenile Jmike Risks Assessments, 16 J. EMPIRICAL LEG. STUD. 175, 184 (2019). 26 See DOJ Report at 29 (referring implicitly to wh